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Inicio  /  Agriculture  /  Vol: 14 Par: 2 (2024)  /  Artículo
ARTÍCULO
TITULO

Prediction Model of Flavonoids Content in Ancient Tree Sun-Dried Green Tea under Abiotic Stress Based on LASSO-Cox

Lei Li    
Yamin Wu    
Houqiao Wang    
Junjie He    
Qiaomei Wang    
Jiayi Xu    
Yuxin Xia    
Wenxia Yuan    
Shuyi Chen    
Lin Tao    
Xinghua Wang and Baijuan Wang    

Resumen

To investigate the variation in flavonoids content in ancient tree sun?dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun-dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, and soil composition of the tree. This study uses two machine-learning models, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression, to build a predictive model based on the selection of effective variables. During the process, bootstrap was used to expand the dataset for single-factor and multi-factor comparative analyses, as well as for model validation, and the goodness-of-fit was assessed using the Akaike information criterion (AIC). The results showed that pH, total potassium, nitrate nitrogen, available phosphorus, hydrolytic nitrogen, and ammonium nitrogen have a high accuracy in predicting the flavonoids content of this model and have a synergistic effect on the production of flavonoids in the ancient tree tea. In this prediction model, when the flavonoids content was >6?, the area under the curve of the training set and validation set were 0.8121 and 0.792 and, when the flavonoids content was >9?, the area under the curve of the training set and validation set were 0.877 and 0.889, demonstrating good consistency. Compared to modeling with all significantly correlated factors (p < 0.05), the AIC decreased by 32.534%. Simultaneously, a visualization system for predicting flavonoids content in ancient tree sun-dried green tea was developed based on a nomogram model. The model was externally validated using actual measurement data and achieved an accuracy rate of 83.33%. Therefore, this study offers a scientific theoretical foundation for explaining the forecast and interference of the quality of ancient tree sun-dried green tea under abiotic stress.

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